Data Scenario and Model Hypothesis

Standard fit report for fits of SISCA to HG_Herring data.

Data Scenario: setup_HG

Model Hypothesis: setup_Aggregate_AR1comps

Species:

Stocks:

Final phase convergence diagnostics

Max Gradient: 0.0019872

Objective Function value: 166.1804007

Time to fit model: 0.06

PD Hessian: FALSE

No. of Non-finite SEs: 1

Model fits

At-a-glance

Time series of spawning biomass with scaled spawn indices (top),
recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for 
substocks of HG_Herring. Stocks are, from left to right,Aggregate.

Figure 1: Time series of spawning biomass with scaled spawn indices (top), recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for substocks of HG_Herring. Stocks are, from left to right,Aggregate.

Fits to data

Model fits to spawn indices.

Figure 2: Model fits to spawn indices.

Average model fits to age data. Stocks are left to right, 
and gears are top to bottom.

Figure 3: Average model fits to age data. Stocks are left to right, and gears are top to bottom.

Model fits to age data, averaged over stock and time. Gears are top to bottom.

Figure 4: Model fits to age data, averaged over stock and time. Gears are top to bottom.

Table 1: Estimated standard deviations for observational data. The first three columns show age data sampling error standard deviations from the logistic-normal compositional likelihood function, and the last column shows spawn survey index standard deviations on the log scale.
\(\tau^{age}_{Red}\) \(\tau^{age}_{SR}\) \(\tau^{age}_{Gn}\) \(\tau^{surv}_{Su}\) \(\tau^{surv}_{D}\)
Aggregate 0.476 0.441 0.646 0.483 0.475

Recruitment

Age-1 recruitments for all stocks. Equilibrium unfished recruitment $R_0$ is 
indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, 
with the average of estimated residuals shown by the horizontal red dashed line.

Figure 5: Age-1 recruitments for all stocks. Equilibrium unfished recruitment \(R_0\) is indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, with the average of estimated residuals shown by the horizontal red dashed line.

Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Figure 6: Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Selectivity and Catch

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Figure 7: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Figure 8: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Figure 9: Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Reference Points

Yield Curves

Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

Figure 10: Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

Stock specific fits

Aggregate

Age composition fits

Model fits to yearly  Aggregate  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 11: Model fits to yearly Aggregate stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to yearly  Aggregate  stock age compositions for the  seineRoe  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 12: Model fits to yearly Aggregate stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to yearly  Aggregate  stock age compositions for the  gillnet  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 13: Model fits to yearly Aggregate stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Age composition residuals for the  Aggregate sub-stock. Positive residuals are black  black, while negative residuals are red.

Figure 14: Age composition residuals for the Aggregate sub-stock. Positive residuals are black black, while negative residuals are red.

Age composition post tail compression

Model fits to tail compressed yearly  Aggregate  stock age compositions for the  reduction  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 15: Model fits to tail compressed yearly Aggregate stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to tail compressed yearly  Aggregate  stock age compositions for the  seineRoe  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 16: Model fits to tail compressed yearly Aggregate stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to tail compressed yearly  Aggregate  stock age compositions for the  gillnet  fleet. Grey bars are age composition  observations, and lines/points are the model expected values.

Figure 17: Model fits to tail compressed yearly Aggregate stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Optimisation performance

Objective function components

Table 2: Objective function components for data observations.
objFun obsSurface obsDive ageRed ageSR ageGill
Total 166.18 -8.2 -6.57 -0.47 -22.88 7.28
Aggregate 166.18 -8.2 -6.57 -0.47 -22.88 7.28
Table 3: Objective function components for standard (single level) and hyper-priors.
V1
objFun 166.180000
recDevs 87.960000
initDevs 10.390000
h 7.950000
M 0.280000
tvMdev 66.600000
IGtau_surf -4.220000
IGtau_dive -4.190000
tvSelAlpha 0.000000
tvSelBeta 0.000000
selAlphaRed 3.470000
selAlphaSR 3.470000
selAlphaGn -0.690000
selBetaRed -1.360000
selBetaSR -1.360000
selBetaGn -0.450000
lnB0 3.033368
lnRinit 5.579506
psiSOK -20.350000
Table 4: Objective function components for hierarchical (mult-level) priors.
V1
objFun 166.18
MDev 0.92
hDev 0.92
selAlphaDevR 0.92
selAlphaDevSR 0.92
selAlphaDevGn 0.92
selBetaDevR 0.92
selBetaDevSR 0.92
selBetaDevGn 0.92

Phase fit table

Table 5: Optimisation performance of SISCA for each phase.
phase objFun maxGrad nPar convCode convMsg time
1 545.6249 0.0000657 3 0 relative convergence (4) 0.0067167
2 544.5749 0.0000884 13 0 relative convergence (4) 0.0042500
3 472.7940 0.0000726 81 0 relative convergence (4) 0.0044667
4 266.2927 0.0001493 146 0 relative convergence (4) 0.0066167
5 266.2927 0.0000750 146 0 relative convergence (4) 0.0042000
6 260.4157 0.0012926 150 0 relative convergence (4) 0.0067000
7 258.5197 0.0005931 153 0 relative convergence (4) 0.0074500
8 166.2809 0.0010105 154 0 relative convergence (4) 0.0094000
9 166.1804 0.0019872 155 0 relative convergence (4) 0.0075333
RE NA NA NA NA NA NA

Leading Parameter SDReport

Table 6: SD report showing leading parameter estimates, standard errors, gradient components, and coefficients of variation. Gradients with a magnitude above 1e-3 are shown in bold red, while the coefficients of variation (cv) are
coloured so that smaller values are lighter in colour, and larger values are darker, with cvs above .5 in bold, and cvs above 3 in red.
est se gr cv
lnB0_p 3.0334 NaN -0.0039 NaN
lnRinit_p 5.5795 0.4453 -0.00024 0.0798
logit_ySteepness 0.7000 0.6327 7.1e-05 0.9038
lnM -1.0985 0.2245 0.0016 0.2044
fDevs_ap 0.5869 0.4777 -4.8e-05 0.814
fDevs_ap.1 -0.2052 0.5021 -4.4e-05 2.4466
fDevs_ap.2 0.3640 0.4819 -3.5e-05 1.324
fDevs_ap.3 1.1545 0.4634 -4.9e-05 0.4014
fDevs_ap.4 -0.0716 0.5191 -1.3e-05 7.2519
fDevs_ap.5 0.3051 0.5165 -6.7e-06 1.6929
fDevs_ap.6 -0.5053 0.8006 -3.3e-06 1.5845
fDevs_ap.7 -0.3259 0.8673 -2.4e-06 2.6616
fDevs_ap.8 -0.2019 0.9162 -1.7e-06 4.5384
fDevs_ap.9 -0.2406 0.9014 -2.5e-06 3.7461
lnSelAlpha_g 0.9967 0.0544 0.00087 0.0546
lnSelAlpha_g.1 1.3465 0.0432 0.0022 0.0321
lnSelAlpha_g.2 1.6508 0.0433 -0.016 0.0262
lnSelBeta_g 0.6738 0.0934 0.00021 0.1386
lnSelBeta_g.1 0.6011 0.0734 3e-05 0.1221
lnSelBeta_g.2 0.2678 0.1148 0.0061 0.4289
lntau2Obs_pg -1.4550 0.1995 6.1e-05 0.1371
lntau2Obs_pg.1 -1.4886 0.2048 -3.3e-05 0.1376
recDevs_vec 2.3259 0.2519 -0.00046 0.1083
recDevs_vec.1 0.3123 0.3144 -8.1e-05 1.0066
recDevs_vec.2 -0.0995 0.3144 3.8e-06 3.1599
recDevs_vec.3 -0.2897 0.2824 -0.00013 0.975
recDevs_vec.4 -0.4131 0.2932 -5.8e-05 0.7098
recDevs_vec.5 1.3815 0.2695 -0.00024 0.1951
recDevs_vec.6 0.0337 0.6519 -4.6e-05 19.3392
recDevs_vec.7 0.8776 0.4813 -0.00021 0.5484
recDevs_vec.8 0.8650 0.4245 -0.00026 0.4907
recDevs_vec.9 0.8925 0.4271 -3e-04 0.4785
recDevs_vec.10 0.5940 0.5341 -0.00028 0.8992
recDevs_vec.11 1.2785 0.4167 -4e-04 0.3259
recDevs_vec.12 -0.8615 0.7473 -2e-05 0.8674
recDevs_vec.13 -1.4533 0.6842 -1e-05 0.4708
recDevs_vec.14 -1.2052 0.6833 -1.2e-05 0.567
recDevs_vec.15 -0.4194 0.5200 -3e-05 1.2398
recDevs_vec.16 0.0129 0.5155 -3.1e-05 40.0259
recDevs_vec.17 0.6841 0.3910 -9.7e-05 0.5715
recDevs_vec.18 0.5262 0.3324 -8.9e-05 0.6318
recDevs_vec.19 1.2485 0.2570 -0.00024 0.2058
recDevs_vec.20 1.1412 0.2552 -0.00042 0.2236
recDevs_vec.21 1.2517 0.2469 -0.00068 0.1972
recDevs_vec.22 -0.3393 0.3017 0.002 0.8892
recDevs_vec.23 -0.1813 0.3206 -0.0019 1.7689
recDevs_vec.24 0.2879 0.3132 0.00061 1.0882
recDevs_vec.25 -0.2763 0.3117 -7e-05 1.1283
recDevs_vec.26 2.8017 0.2727 0.0017 0.0974
recDevs_vec.27 0.5840 0.2766 -4.5e-05 0.4735
recDevs_vec.28 -0.4973 0.2831 0.00022 0.5692
recDevs_vec.29 -0.7079 0.2884 -0.00025 0.4075
recDevs_vec.30 1.3470 0.2697 -0.00086 0.2002
recDevs_vec.31 0.5006 0.2717 -0.00054 0.5428
recDevs_vec.32 -1.0913 0.3029 -0.00059 0.2775
recDevs_vec.33 -0.6847 0.2637 -0.00027 0.3852
recDevs_vec.34 1.6869 0.2336 -0.00057 0.1385
recDevs_vec.35 0.6303 0.2365 0.00098 0.3752
recDevs_vec.36 -0.2612 0.2460 0.00029 0.9418
recDevs_vec.37 -1.3080 0.2805 5.5e-05 0.2144
recDevs_vec.38 0.7195 0.2570 -4.9e-05 0.3572
recDevs_vec.39 -1.8275 0.3590 2.8e-06 0.1964
recDevs_vec.40 -1.8827 0.3675 -0.00027 0.1952
recDevs_vec.41 -1.0431 0.3474 -0.00019 0.333
recDevs_vec.42 0.2066 0.2801 -0.00019 1.3559
recDevs_vec.43 0.4638 0.2750 -4.2e-08 0.5929
recDevs_vec.44 1.5501 0.2453 5e-04 0.1582
recDevs_vec.45 -1.6222 0.3081 -4e-05 0.1899
recDevs_vec.46 -0.4206 0.2942 -6e-05 0.6994
recDevs_vec.47 -0.4245 0.3024 9e-05 0.7123
recDevs_vec.48 -0.2809 0.3026 -1.3e-06 1.0771
recDevs_vec.49 0.8457 0.2849 -2.6e-05 0.3369
recDevs_vec.50 -0.7796 0.4085 -1.3e-05 0.5239
recDevs_vec.51 0.5261 0.3649 1.4e-06 0.6936
recDevs_vec.52 -0.7901 0.4461 -1.2e-05 0.5646
recDevs_vec.53 0.7609 0.3792 -7.5e-06 0.4984
recDevs_vec.54 -0.8982 0.3911 -5.8e-05 0.4355
recDevs_vec.55 0.8317 0.3401 1.5e-05 0.4089
recDevs_vec.56 -1.1114 0.4217 -1.4e-05 0.3794
recDevs_vec.57 0.3725 0.3490 1.4e-05 0.9369
recDevs_vec.58 -0.2121 0.3706 4.2e-07 1.7475
recDevs_vec.59 1.3819 0.3100 1.8e-05 0.2243
recDevs_vec.60 -0.6795 0.3839 -1.4e-05 0.565
recDevs_vec.61 -0.1432 0.3649 -2e-06 2.5481
omegaM_pt -0.1803 0.9778 0.00016 5.4231
omegaM_pt.1 -0.1620 0.9772 0.00016 6.031
omegaM_pt.2 -0.0404 0.9751 0.00015 24.1497
omegaM_pt.3 0.0750 0.9731 0.00013 12.9753
omegaM_pt.4 0.2412 0.9687 0.00011 4.0161
omegaM_pt.5 0.5365 0.9624 9.5e-05 1.7938
omegaM_pt.6 0.4763 0.9605 9.3e-05 2.0168
omegaM_pt.7 0.3337 0.9613 8.7e-05 2.8809
omegaM_pt.8 0.3522 0.9629 8.6e-05 2.734
omegaM_pt.9 0.2884 0.9647 7.7e-05 3.3453
omegaM_pt.10 0.2451 0.9655 5.6e-05 3.9384
omegaM_pt.11 0.2346 0.9633 2.3e-05 4.1065
omegaM_pt.12 0.3670 0.9581 -1.9e-05 2.6104
omegaM_pt.13 0.5924 0.9534 -7e-05 1.6094
omegaM_pt.14 0.7305 0.9547 -9.7e-05 1.3069
omegaM_pt.15 0.4189 0.9635 -8.7e-05 2.3001
omegaM_pt.16 -0.0082 0.9678 -7.9e-05 118.3521
omegaM_pt.17 -0.2381 0.9663 -7.2e-05 4.0582
omegaM_pt.18 -0.2798 0.9610 -6.6e-05 3.4346
omegaM_pt.19 -0.2404 0.9576 -6.5e-05 3.9825
omegaM_pt.20 -0.2629 0.9569 -6.8e-05 3.6403
omegaM_pt.21 -0.3927 0.9550 -8.2e-05 2.4318
omegaM_pt.22 -0.4338 0.9502 -0.00012 2.1903
omegaM_pt.23 -0.2809 0.9447 -0.00018 3.3628
omegaM_pt.24 -0.1448 0.9412 -0.00014 6.5016
omegaM_pt.25 0.0375 0.9391 -0.00019 25.0518
omegaM_pt.26 0.1970 0.9379 -0.00021 4.7598
omegaM_pt.27 0.2428 0.9364 -0.00022 3.857
omegaM_pt.28 0.2504 0.9372 -0.00014 3.7431
omegaM_pt.29 0.2998 0.9393 -5.4e-05 3.1329
omegaM_pt.30 0.3167 0.9403 3.5e-05 2.969
omegaM_pt.31 0.3734 0.9394 0.00011 2.5159
omegaM_pt.32 0.4609 0.9379 0.00013 2.0349
omegaM_pt.33 0.5077 0.9373 0.00012 1.846
omegaM_pt.34 0.3646 0.9366 6.7e-05 2.5686
omegaM_pt.35 0.0522 0.9370 -1.1e-05 17.959
omegaM_pt.36 -0.0266 0.9358 -0.00013 35.1357
omegaM_pt.37 -0.1006 0.9350 -0.00018 9.2915
omegaM_pt.38 0.1091 0.9293 -0.00021 8.5152
omegaM_pt.39 0.3300 0.9233 -0.00022 2.7976
omegaM_pt.40 0.4544 0.9251 -0.00022 2.0359
omegaM_pt.41 0.4360 0.9269 -0.00021 2.1259
omegaM_pt.42 0.4004 0.9250 -0.00021 2.3103
omegaM_pt.43 0.4012 0.9256 -0.00023 2.3068
omegaM_pt.44 0.1728 0.9281 -0.00027 5.3716
omegaM_pt.45 0.1155 0.9277 -0.00031 8.0322
omegaM_pt.46 0.3162 0.9232 -0.00031 2.9199
omegaM_pt.47 0.5047 0.9181 -0.00032 1.8191
omegaM_pt.48 0.5565 0.9134 -0.00032 1.6414
omegaM_pt.49 0.5682 0.9144 -3e-04 1.6094
omegaM_pt.50 0.5186 0.9237 -0.00028 1.7812
omegaM_pt.51 0.0801 0.9259 -0.00026 11.5609
omegaM_pt.52 -0.0413 0.9264 -0.00025 22.4401
omegaM_pt.53 -0.1742 0.9253 -0.00023 5.3131
omegaM_pt.54 -0.4433 0.9277 -0.00022 2.0931
omegaM_pt.55 -0.4118 0.9267 -2e-04 2.2502
omegaM_pt.56 -0.3699 0.9278 -0.00019 2.5084
omegaM_pt.57 -0.3305 0.9279 -0.00018 2.8077
omegaM_pt.58 -0.2257 0.9276 -0.00017 4.1107
omegaM_pt.59 -0.1379 0.9260 -0.00016 6.7131
omegaM_pt.60 -0.0446 0.9244 -0.00014 20.7453
omegaM_pt.61 0.2067 0.9231 -0.00013 4.4664
omegaM_pt.62 0.4670 0.9241 -0.00011 1.9787
omegaM_pt.63 0.7966 0.9185 -9.8e-05 1.153
omegaM_pt.64 0.5271 0.9308 -7.9e-05 1.7657
omegaM_pt.65 -0.0164 0.9444 -5.9e-05 57.5531
omegaM_pt.66 -0.2331 0.9654 -3.8e-05 4.1407
omegaM_pt.67 -0.2387 0.9818 -1.8e-05 4.1135
logitphi1_g 0.6199 0.5531 2.7e-06 0.8921
logitphi1_g.1 0.5844 0.2425 2e-05 0.415
logitphi1_g.2 0.6909 0.4435 -0.0059 0.6419

MCMC posteriors

MCMC performance

## Not Yet Implemented

Other

Compositional Likelihood Correlation Matrices

Estimated correlation matrices for age composition residuals in the  reduction  fleet. The circles above the visualise the numbers below the diagonal.

Figure 18: Estimated correlation matrices for age composition residuals in the reduction fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the  seineRoe  fleet. The circles above the visualise the numbers below the diagonal.

Figure 19: Estimated correlation matrices for age composition residuals in the seineRoe fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the  gillnet  fleet. The circles above the visualise the numbers below the diagonal.

Figure 20: Estimated correlation matrices for age composition residuals in the gillnet fleet. The circles above the visualise the numbers below the diagonal.

Compositional Likelihood Diagnostic Plot

Diagnostic plot for compositional likelihood function.

Figure 21: Diagnostic plot for compositional likelihood function.

Comparisons with ISCAM

Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Figure 22: Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.

Figure 23: Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.